Artificial intelligence (AI) has an increasingly important role in terms of automating and optimizing processes in research and industrial laboratories. One process that is gaining popularity is liquid handling, where AI utilization is combined with robotic systems. This can help laboratories achieve higher accuracy and efficiency as well as enabling complex and high-throughput tasks to be done more easily.
What are liquid handling robots?
It is important to note that a liquid handling robot is an automated system intended to manipulate and manage liquids in laboratory settings more precisely. Basically, they can streamline tasks, like dispensing, mixing, and pipetting. This is vital in improving the accuracy and efficiency of experiments and analyses.
Generally, this concept uses a range of technologies and tools for automating liquid transfer. It works by using a pipetting mechanism combined with robotic arms and integrated software systems. There are several types of these new technological devices designed for different scales and types of tasks in the laboratory.
Enhanced accuracy and precision
The algorithms in AI can help in the calibration of liquid handling robots so that precise measurements are achieved. Likewise, AI-driven systems are able to learn from previous operations in order to adjust for variable factors like pressure or viscosity that can greatly affect liquid handling accuracy. As a result, it enables higher reproducibility in experiments, as it makes generated data more reliable.
Optimization of the automated process
Unlike the manual processes, AI is able to analyze huge datasets that are generated during the process of handling liquid. This helps in identifying patterns and optimizing protocols in order to improve efficiency in the workflow. So, it can reduce the time required in setting up experiments and help in gaining faster turnarounds, particularly in repetitive or high-throughput tasks.
Real-time detection and correction of errors
AI with machine vision, for instance, can monitor relevant tasks in real-time. This will be able to detect potential errors, such as bubbles or incorrect liquid levels. When the errors are identified, the AI system can adjust the process immediately or alert the operator. Consequently, it will minimize waste and avoid disruptions in experiments.
Adaptive learning and customisation
This new technology can adapt to specific lab environments through machine learning. It can also adapt to user preferences over time, so that liquid handling protocols can meet unique requirements. This is a valuable trait in research because research directions or new experiments need to adapt to protocols.
Laboratory information management system (LIMS) integration
This new mobile technology can also be integrated with LIMS so that records updating and dataflow management can be done throughout the research process. As a result, it can achieve better data management so that researchers can track the progress of experiments. Moreover, it can maintain accurate sample records and make sure they meet the industry standards.
Achieve high-throughput screening and drug identification
In pharmaceutical research, for example, liquid handling robots are quite useful in throughput screening processes. This is where thousands of samples require quick and accurate processing. In addition to that, the algorithms in AI can design and optimize plate layouts, automating the identification of potential drug candidates from huge compound libraries.
Liquid handling equipment maintenance prediction
One of the valuable traits of AI with its integration into liquid handling tools is the prediction of maintenance when needed on the basis of usage patterns or performance metrics. Note that this can reduce the risk of unexpected equipment failure that can possibly disrupt ongoing experiments.
More importantly, this approach can help maintain optimal productivity in laboratories and reduce associated costs for unplanned downtime or emergency repairs.